Forecasting Solar Energy Production using a Hybrid GCN-BiLSTM Model

Pamungkas, RF; Yoga Utama, IBK; Faridh, MM; Alam, MM; Chung, B; Jang, YM

Forecasting Solar Energy Production using a Hybrid GCN-BiLSTM Model

Pamungkas, RF Yoga Utama, IBK Faridh, MM Alam, MM Chung, B Jang, YM

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2023, 00, pp. 53-56
Issue Date:: 2023-03-23

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Field	Value	Language
dc.contributor.author	Pamungkas, RF
dc.contributor.author	Yoga Utama, IBK
dc.contributor.author	Faridh, MM
dc.contributor.author	Alam, MM
dc.contributor.author	Chung, B
dc.contributor.author	Jang, YM
dc.date	2023-02-20
dc.date.accessioned	2024-05-03T01:59:41Z
dc.date.available	2024-05-03T01:59:41Z
dc.date.issued	2023-03-23
dc.identifier.citation	2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2023, 00, pp. 53-56
dc.identifier.isbn	978-1-6654-5646-3
dc.identifier.issn	2831-6991
dc.identifier.uri	http://hdl.handle.net/10453/178576
dc.description.abstract	Under increasing levels of renewable energy source RES penetration unpredictability and uncertainty are emerging drivers of power imbalances Forecasting is frequently used to anticipate renewable energy power generation Forecast errors on the other hand significantly negatively impact power system performance This research describes a deep learning technique based on spatiotemporal analysis for accurately forecasting solar power generation Solar power generation output from seven PV sites is predicted using a hybrid graph convolutional network GCN module bidirectional long short term memory BiLSTM module and attention layer Our model effectively captures comprehensive spatiotemporal correlations on real world solar power generation datasets and surpasses several existing methods
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
dc.relation.ispartof	2023 International Conference on Artificial Intelligence in Information and Communication
dc.relation.ispartofseries	Conference on Artificial Intelligence in Information and Communication
dc.relation.isbasedon	10.1109/icaiic57133.2023.10067088
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Forecasting Solar Energy Production using a Hybrid GCN-BiLSTM Model
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Bali, Indonesia
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-03T01:59:39Z
pubs.finish-date	2023-02-23
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-02-20
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Under increasing levels of renewable energy source RES penetration unpredictability and uncertainty are emerging drivers of power imbalances Forecasting is frequently used to anticipate renewable energy power generation Forecast errors on the other hand significantly negatively impact power system performance This research describes a deep learning technique based on spatiotemporal analysis for accurately forecasting solar power generation Solar power generation output from seven PV sites is predicted using a hybrid graph convolutional network GCN module bidirectional long short term memory BiLSTM module and attention layer Our model effectively captures comprehensive spatiotemporal correlations on real world solar power generation datasets and surpasses several existing methods

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178576